{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "61239b7d",
   "metadata": {},
   "source": [
    "# GoogLeNet\n",
    "\n",
    "GoogLeNet（又名Inception v1）由Google团队于2014年提出，是当年ImageNet挑战赛（ILSVRC14）的冠军模型。在此之前，主流的CNN模型如AlexNet和VGG通过增加网络深度提升性能，但问题显著：  \n",
    "- **参数爆炸与过拟合**：VGG参数量(VGG19, ~144M)是AlexNet(~60M)的2+倍，导致计算资源消耗巨大且容易在小数据集上过拟合。  \n",
    "- **梯度消失**：网络深度增加时，反向传播的梯度难以有效传递至浅层，导致训练效率低下。  \n",
    "- **信息丢失**：串联式网络结构中，深层无法恢复浅层丢失的细节特征。  \n",
    "\n",
    "GoogLeNet的核心目标是通过**高效利用计算资源**，在控制参数量的同时提升模型性能，其解决方案是引入**Inception模块**和**辅助分类器**.\n",
    "\n",
    "### 关键创新点：  \n",
    "- **Inception模块**  \n",
    "  并行使用不同尺寸的卷积核（1×1、3×3、5×5）和池化层捕捉多尺度特征，并通过1×1卷积降维控制计算量。例如，5×5卷积前使用1×1卷积将通道数从512降至16，参数量减少。 \n",
    "- **辅助分类器**  \n",
    "  在网络中部加入两个辅助分类器，通过加权损失缓解梯度消失问题，并起到正则化作用。  \n",
    "- **全局平均池化**  \n",
    "  取代全连接层，直接对特征图进行平均池化，参数量减少90%以上，同时降低过拟合风险。  \n",
    "- **模块化设计**  \n",
    "  Inception模块的堆叠使网络结构灵活可扩展，为后续模块化设计奠定基础。  \n",
    "\n",
    "### 对CNN发展的影响：  \n",
    "- 启发了Inception v3、ResNet等模型对多尺度特征的重视  \n",
    "- 稀疏连接设计和全局平均池化成为现代CNN的标配  \n",
    "- 推动了移动端轻量化模型（如MobileNet）的发展\n",
    "\n",
    "![alt text](resources/GooLeNet_arch.png \"Title\")\n",
    "\n",
    "---\n",
    "\n",
    "## 与VGGNet的优势对比  \n",
    "\n",
    "| 维度         | GoogLeNet                                  | VGGNet                     |\n",
    "|--------------|--------------------------------------------|----------------------------|\n",
    "| **参数量**   | 6M         | 144M    |\n",
    "| **计算效率** | 1×1卷积和Inception模块降低计算复杂度       | 3×3卷积堆叠导致计算量陡增  |\n",
    "| **结构设计** | 并行多尺度特征提取，信息保留更全面         | 单一尺度卷积，深层易丢细节 |\n",
    "| **训练稳定性** | 辅助分类器缓解梯度消失，加速收敛           | 依赖ReLU和梯度裁剪         |\n",
    "| **扩展性**   | 模块化设计便于调整深度与宽度               | 固定3×3卷积，扩展性受限    |\n",
    "\n",
    "---\n",
    "\n",
    "## 总结  \n",
    "GoogLeNet通过**多尺度特征融合**与**高效参数设计**，在深度学习史上树立了里程碑。其核心理念不仅解决了当时网络的瓶颈，更为后续模型的创新提供了范式。与VGG相比，GoogLeNet在参数效率、训练稳定性和结构灵活性上更具优势，成为资源敏感场景（如移动端部署）的重要架构。\n",
    "\n",
    "具体的参数如下:\n",
    "\n",
    "![alt text](resources/googlenet_table.png \"Title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f238925f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "from hdd.data_util.transforms import RandomResize\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)\n",
    "\n",
    "\n",
    "\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        RandomResize([256, 296, 384]),  # 随机在三个size中选择一个进行resize\n",
    "        transforms.RandomRotation(10),\n",
    "        transforms.RandomCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "val_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    ImagenetteInMemory(\n",
    "        root=DATA_ROOT,\n",
    "        split=\"train\",\n",
    "        size=\"full\",\n",
    "        download=True,\n",
    "        transform=train_dataset_transforms,\n",
    "    ),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    num_workers=8,\n",
    "    pin_memory=True,\n",
    ")\n",
    "val_dataloader = torch.utils.data.DataLoader(\n",
    "    ImagenetteInMemory(\n",
    "        root=DATA_ROOT,\n",
    "        split=\"val\",\n",
    "        size=\"full\",\n",
    "        download=True,\n",
    "        transform=val_dataset_transforms,\n",
    "    ),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=False,\n",
    "    num_workers=8,\n",
    "    pin_memory=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24adf9bd",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9857c77e",
   "metadata": {},
   "source": [
    "GoogleNet中的基础模块有很多版面,我们选择性地查看几个版本。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92523162",
   "metadata": {},
   "source": [
    "### Inception V1\n",
    "\n",
    "从下图可以清晰看到V1模型的多尺度特征融合和采用1x1卷积降低运算量的结构\n",
    "\n",
    "![alt text](resources/inceptionV1.png \"Title\")\n",
    "\n",
    "代码如下:\n",
    "\n",
    "```python\n",
    "class InceptionV1(nn.Module):\n",
    "    def __init__(self, in_channel, ch_1x1, ch_3x3_reduce, ch_3x3, ch_5x5_reduce, ch_5x5, ch_pool_proj,\n",
    "    ) -> None:\n",
    "        super().__init__()\n",
    "        self.branch1 = InceptionConv2d(in_channel, ch_1x1, kernel_size=1)\n",
    "        self.branch2 = nn.Sequential(\n",
    "            InceptionConv2d(in_channel, ch_3x3_reduce, kernel_size=1),\n",
    "            InceptionConv2d(ch_3x3_reduce, ch_3x3, kernel_size=3, padding=1),\n",
    "        )\n",
    "        self.branch3 = nn.Sequential(\n",
    "            InceptionConv2d(in_channel, ch_5x5_reduce, kernel_size=1),\n",
    "            InceptionConv2d(ch_5x5_reduce, ch_5x5, kernel_size=5, padding=2),\n",
    "        )\n",
    "        self.branch4 = nn.Sequential(\n",
    "            nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),\n",
    "            InceptionConv2d(in_channel, ch_pool_proj, kernel_size=1),\n",
    "        )\n",
    "\n",
    "    def forward(self, X) -> torch.Tensor:\n",
    "        branch1 = self.branch1(X)\n",
    "        branch2 = self.branch2(X)\n",
    "        branch3 = self.branch3(X)\n",
    "        branch4 = self.branch4(X)\n",
    "        return torch.cat([branch1, branch2, branch3, branch4], dim=1)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4736bc7",
   "metadata": {},
   "source": [
    "**不使用Auxiliary Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43844be5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable parameter in GoogLeNet V1:  5991082\n",
      "Epoch: 1/100 Train Loss: 1.7983 Accuracy: 0.3727 Time: 8.04973  | Val Loss: 1.9290 Accuracy: 0.4237\n",
      "Epoch: 2/100 Train Loss: 1.3238 Accuracy: 0.5636 Time: 7.47678  | Val Loss: 1.5129 Accuracy: 0.5414\n",
      "Epoch: 3/100 Train Loss: 1.1385 Accuracy: 0.6302 Time: 7.48562  | Val Loss: 1.4758 Accuracy: 0.5327\n",
      "Epoch: 4/100 Train Loss: 0.9958 Accuracy: 0.6835 Time: 7.47752  | Val Loss: 1.5098 Accuracy: 0.5822\n",
      "Epoch: 5/100 Train Loss: 0.9028 Accuracy: 0.7116 Time: 7.56000  | Val Loss: 1.1462 Accuracy: 0.6443\n",
      "Epoch: 6/100 Train Loss: 0.8355 Accuracy: 0.7320 Time: 7.69116  | Val Loss: 0.8913 Accuracy: 0.7243\n",
      "Epoch: 7/100 Train Loss: 0.7624 Accuracy: 0.7567 Time: 7.53098  | Val Loss: 0.8071 Accuracy: 0.7442\n",
      "Epoch: 8/100 Train Loss: 0.7465 Accuracy: 0.7609 Time: 7.55076  | Val Loss: 0.8965 Accuracy: 0.7389\n",
      "Epoch: 9/100 Train Loss: 0.7034 Accuracy: 0.7738 Time: 7.44836  | Val Loss: 0.6561 Accuracy: 0.7903\n",
      "Epoch: 10/100 Train Loss: 0.6425 Accuracy: 0.7958 Time: 7.50443  | Val Loss: 0.7728 Accuracy: 0.7562\n",
      "Epoch: 11/100 Train Loss: 0.6237 Accuracy: 0.8004 Time: 7.42629  | Val Loss: 0.6935 Accuracy: 0.7842\n",
      "Epoch: 12/100 Train Loss: 0.5948 Accuracy: 0.8130 Time: 7.46781  | Val Loss: 0.7124 Accuracy: 0.7783\n",
      "Epoch: 13/100 Train Loss: 0.5535 Accuracy: 0.8215 Time: 7.49661  | Val Loss: 0.6369 Accuracy: 0.8061\n",
      "Epoch: 14/100 Train Loss: 0.5766 Accuracy: 0.8165 Time: 7.40214  | Val Loss: 0.6101 Accuracy: 0.7931\n",
      "Epoch: 15/100 Train Loss: 0.5169 Accuracy: 0.8321 Time: 7.45016  | Val Loss: 0.5890 Accuracy: 0.8092\n",
      "Epoch: 16/100 Train Loss: 0.5029 Accuracy: 0.8386 Time: 7.64253  | Val Loss: 0.5913 Accuracy: 0.8166\n",
      "Epoch: 17/100 Train Loss: 0.4941 Accuracy: 0.8367 Time: 7.34722  | Val Loss: 0.5368 Accuracy: 0.8265\n",
      "Epoch: 18/100 Train Loss: 0.4882 Accuracy: 0.8420 Time: 7.51122  | Val Loss: 0.6129 Accuracy: 0.8025\n",
      "Epoch: 19/100 Train Loss: 0.4626 Accuracy: 0.8500 Time: 7.31274  | Val Loss: 0.7050 Accuracy: 0.7890\n",
      "Epoch: 20/100 Train Loss: 0.4613 Accuracy: 0.8534 Time: 7.45867  | Val Loss: 0.5489 Accuracy: 0.8329\n",
      "Epoch: 21/100 Train Loss: 0.3521 Accuracy: 0.8856 Time: 7.45880  | Val Loss: 0.3691 Accuracy: 0.8790\n",
      "Epoch: 22/100 Train Loss: 0.3085 Accuracy: 0.9013 Time: 7.33684  | Val Loss: 0.3596 Accuracy: 0.8828\n",
      "Epoch: 23/100 Train Loss: 0.2888 Accuracy: 0.9075 Time: 7.31979  | Val Loss: 0.3465 Accuracy: 0.8843\n",
      "Epoch: 24/100 Train Loss: 0.2808 Accuracy: 0.9107 Time: 7.39137  | Val Loss: 0.3536 Accuracy: 0.8841\n",
      "Epoch: 25/100 Train Loss: 0.2679 Accuracy: 0.9170 Time: 7.43335  | Val Loss: 0.3446 Accuracy: 0.8879\n",
      "Epoch: 26/100 Train Loss: 0.2613 Accuracy: 0.9170 Time: 7.28092  | Val Loss: 0.3456 Accuracy: 0.8864\n",
      "Epoch: 27/100 Train Loss: 0.2590 Accuracy: 0.9171 Time: 7.62523  | Val Loss: 0.3542 Accuracy: 0.8838\n",
      "Epoch: 28/100 Train Loss: 0.2600 Accuracy: 0.9177 Time: 7.58915  | Val Loss: 0.3562 Accuracy: 0.8848\n",
      "Epoch: 29/100 Train Loss: 0.2508 Accuracy: 0.9197 Time: 7.49383  | Val Loss: 0.3358 Accuracy: 0.8902\n",
      "Epoch: 30/100 Train Loss: 0.2523 Accuracy: 0.9189 Time: 7.48211  | Val Loss: 0.3537 Accuracy: 0.8828\n",
      "Epoch: 31/100 Train Loss: 0.2435 Accuracy: 0.9221 Time: 7.63082  | Val Loss: 0.3409 Accuracy: 0.8899\n",
      "Epoch: 32/100 Train Loss: 0.2319 Accuracy: 0.9265 Time: 7.34009  | Val Loss: 0.3456 Accuracy: 0.8887\n",
      "Epoch: 33/100 Train Loss: 0.2366 Accuracy: 0.9257 Time: 7.38492  | Val Loss: 0.3444 Accuracy: 0.8902\n",
      "Epoch: 34/100 Train Loss: 0.2238 Accuracy: 0.9295 Time: 7.48531  | Val Loss: 0.3520 Accuracy: 0.8887\n",
      "Epoch: 35/100 Train Loss: 0.2203 Accuracy: 0.9286 Time: 7.47027  | Val Loss: 0.3575 Accuracy: 0.8864\n",
      "Epoch: 36/100 Train Loss: 0.2225 Accuracy: 0.9300 Time: 7.60273  | Val Loss: 0.3450 Accuracy: 0.8889\n",
      "Epoch: 37/100 Train Loss: 0.2210 Accuracy: 0.9300 Time: 7.35410  | Val Loss: 0.3421 Accuracy: 0.8932\n",
      "Epoch: 38/100 Train Loss: 0.2148 Accuracy: 0.9307 Time: 7.31505  | Val Loss: 0.3477 Accuracy: 0.8884\n",
      "Epoch: 39/100 Train Loss: 0.2087 Accuracy: 0.9335 Time: 7.37529  | Val Loss: 0.3602 Accuracy: 0.8854\n",
      "Epoch: 40/100 Train Loss: 0.2165 Accuracy: 0.9320 Time: 7.53705  | Val Loss: 0.3405 Accuracy: 0.8938\n",
      "Epoch: 41/100 Train Loss: 0.2044 Accuracy: 0.9358 Time: 7.58980  | Val Loss: 0.3404 Accuracy: 0.8925\n",
      "Epoch: 42/100 Train Loss: 0.2032 Accuracy: 0.9371 Time: 7.42748  | Val Loss: 0.3362 Accuracy: 0.8912\n",
      "Epoch: 43/100 Train Loss: 0.1929 Accuracy: 0.9399 Time: 7.52256  | Val Loss: 0.3326 Accuracy: 0.8935\n",
      "Epoch: 44/100 Train Loss: 0.1900 Accuracy: 0.9413 Time: 7.50479  | Val Loss: 0.3331 Accuracy: 0.8938\n",
      "Epoch: 45/100 Train Loss: 0.1896 Accuracy: 0.9378 Time: 7.35962  | Val Loss: 0.3349 Accuracy: 0.8930\n",
      "Epoch: 46/100 Train Loss: 0.1878 Accuracy: 0.9413 Time: 7.43131  | Val Loss: 0.3334 Accuracy: 0.8943\n",
      "Epoch: 47/100 Train Loss: 0.1854 Accuracy: 0.9428 Time: 7.54082  | Val Loss: 0.3386 Accuracy: 0.8922\n",
      "Epoch: 48/100 Train Loss: 0.1902 Accuracy: 0.9402 Time: 7.35436  | Val Loss: 0.3378 Accuracy: 0.8920\n",
      "Epoch: 49/100 Train Loss: 0.1888 Accuracy: 0.9408 Time: 7.37029  | Val Loss: 0.3347 Accuracy: 0.8950\n",
      "Epoch: 50/100 Train Loss: 0.1954 Accuracy: 0.9383 Time: 7.43547  | Val Loss: 0.3334 Accuracy: 0.8953\n",
      "Epoch: 51/100 Train Loss: 0.1920 Accuracy: 0.9411 Time: 7.38872  | Val Loss: 0.3350 Accuracy: 0.8935\n",
      "Epoch: 52/100 Train Loss: 0.1889 Accuracy: 0.9417 Time: 7.39312  | Val Loss: 0.3334 Accuracy: 0.8948\n",
      "Epoch: 53/100 Train Loss: 0.1996 Accuracy: 0.9380 Time: 7.46249  | Val Loss: 0.3355 Accuracy: 0.8932\n",
      "Epoch: 54/100 Train Loss: 0.1962 Accuracy: 0.9384 Time: 7.47806  | Val Loss: 0.3371 Accuracy: 0.8904\n",
      "Epoch: 55/100 Train Loss: 0.1782 Accuracy: 0.9422 Time: 7.66210  | Val Loss: 0.3333 Accuracy: 0.8935\n",
      "Epoch: 56/100 Train Loss: 0.1931 Accuracy: 0.9389 Time: 7.49568  | Val Loss: 0.3406 Accuracy: 0.8938\n",
      "Epoch: 57/100 Train Loss: 0.1873 Accuracy: 0.9411 Time: 7.58367  | Val Loss: 0.3331 Accuracy: 0.8927\n",
      "Epoch: 58/100 Train Loss: 0.1791 Accuracy: 0.9443 Time: 7.54409  | Val Loss: 0.3357 Accuracy: 0.8938\n",
      "Early stop at epoch 58!\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.googlenet import GoogLeNet, InceptionV1\n",
    "from hdd.train.early_stopping import EarlyStoppingInMem\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "net = GoogLeNet(InceptionV1, num_classes=10, dropout=0.5, use_aux=False).to(DEVICE)\n",
    "print(\"Trainable parameter in GoogLeNet V1: \", count_trainable_parameter(net))\n",
    "criteria = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "    optimizer, step_size=20, gamma=0.1, last_epoch=-1\n",
    ")\n",
    "early_stopper = EarlyStoppingInMem(patience=15, verbose=False)\n",
    "max_epochs = 100\n",
    "training_stats_v1 = naive_train_classification_model(\n",
    "    net,\n",
    "    criteria,\n",
    "    max_epochs,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    DEVICE,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    early_stopper,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a4f8669",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy V1: 0.8935031847133758\n"
     ]
    }
   ],
   "source": [
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"Accuracy V1: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b645ae2",
   "metadata": {},
   "source": [
    "**使用Auxiliary Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1135b08f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/100 Train Loss: 1.8044 Accuracy: 0.3699 Time: 7.61825  | Val Loss: 2.4739 Accuracy: 0.3320\n",
      "Epoch: 2/100 Train Loss: 1.3031 Accuracy: 0.5741 Time: 7.53379  | Val Loss: 1.7776 Accuracy: 0.5203\n",
      "Epoch: 3/100 Train Loss: 1.1201 Accuracy: 0.6392 Time: 7.50013  | Val Loss: 1.1703 Accuracy: 0.6318\n",
      "Epoch: 4/100 Train Loss: 0.9779 Accuracy: 0.6843 Time: 7.20864  | Val Loss: 0.8788 Accuracy: 0.7223\n",
      "Epoch: 5/100 Train Loss: 0.9226 Accuracy: 0.7042 Time: 7.19515  | Val Loss: 0.8242 Accuracy: 0.7442\n",
      "Epoch: 6/100 Train Loss: 0.8226 Accuracy: 0.7413 Time: 7.17597  | Val Loss: 0.9733 Accuracy: 0.6968\n",
      "Epoch: 7/100 Train Loss: 0.7637 Accuracy: 0.7554 Time: 7.10189  | Val Loss: 0.9390 Accuracy: 0.7218\n",
      "Epoch: 8/100 Train Loss: 0.7025 Accuracy: 0.7763 Time: 7.26789  | Val Loss: 0.7963 Accuracy: 0.7592\n",
      "Epoch: 9/100 Train Loss: 0.6627 Accuracy: 0.7867 Time: 7.11924  | Val Loss: 0.9553 Accuracy: 0.6966\n",
      "Epoch: 10/100 Train Loss: 0.6592 Accuracy: 0.7875 Time: 7.32551  | Val Loss: 0.9897 Accuracy: 0.7149\n",
      "Epoch: 11/100 Train Loss: 0.6151 Accuracy: 0.7972 Time: 7.25554  | Val Loss: 0.9713 Accuracy: 0.7134\n",
      "Epoch: 12/100 Train Loss: 0.6035 Accuracy: 0.8023 Time: 7.10179  | Val Loss: 0.6857 Accuracy: 0.7954\n",
      "Epoch: 13/100 Train Loss: 0.5631 Accuracy: 0.8190 Time: 7.27923  | Val Loss: 0.6997 Accuracy: 0.7949\n",
      "Epoch: 14/100 Train Loss: 0.5361 Accuracy: 0.8250 Time: 7.22129  | Val Loss: 0.6210 Accuracy: 0.8056\n",
      "Epoch: 15/100 Train Loss: 0.5142 Accuracy: 0.8367 Time: 7.16116  | Val Loss: 0.6163 Accuracy: 0.8099\n",
      "Epoch: 16/100 Train Loss: 0.4868 Accuracy: 0.8413 Time: 7.35974  | Val Loss: 0.5965 Accuracy: 0.8059\n",
      "Epoch: 17/100 Train Loss: 0.4906 Accuracy: 0.8401 Time: 7.30645  | Val Loss: 0.7159 Accuracy: 0.7745\n",
      "Epoch: 18/100 Train Loss: 0.4551 Accuracy: 0.8505 Time: 7.12136  | Val Loss: 0.5882 Accuracy: 0.8321\n",
      "Epoch: 19/100 Train Loss: 0.4318 Accuracy: 0.8601 Time: 7.20109  | Val Loss: 0.7215 Accuracy: 0.7941\n",
      "Epoch: 20/100 Train Loss: 0.4462 Accuracy: 0.8569 Time: 7.15314  | Val Loss: 0.7062 Accuracy: 0.7870\n",
      "Epoch: 21/100 Train Loss: 0.3291 Accuracy: 0.8975 Time: 7.31099  | Val Loss: 0.3443 Accuracy: 0.8869\n",
      "Epoch: 22/100 Train Loss: 0.3000 Accuracy: 0.9033 Time: 7.23567  | Val Loss: 0.3360 Accuracy: 0.8912\n",
      "Epoch: 23/100 Train Loss: 0.2786 Accuracy: 0.9117 Time: 7.32089  | Val Loss: 0.3311 Accuracy: 0.8920\n",
      "Epoch: 24/100 Train Loss: 0.2698 Accuracy: 0.9129 Time: 7.21362  | Val Loss: 0.3263 Accuracy: 0.8950\n",
      "Epoch: 25/100 Train Loss: 0.2603 Accuracy: 0.9182 Time: 7.33351  | Val Loss: 0.3359 Accuracy: 0.8915\n",
      "Epoch: 26/100 Train Loss: 0.2437 Accuracy: 0.9210 Time: 7.20825  | Val Loss: 0.3301 Accuracy: 0.8961\n",
      "Epoch: 27/100 Train Loss: 0.2437 Accuracy: 0.9228 Time: 7.30036  | Val Loss: 0.3340 Accuracy: 0.8973\n",
      "Epoch: 28/100 Train Loss: 0.2432 Accuracy: 0.9246 Time: 7.19536  | Val Loss: 0.3330 Accuracy: 0.8966\n",
      "Epoch: 29/100 Train Loss: 0.2304 Accuracy: 0.9289 Time: 7.14253  | Val Loss: 0.3347 Accuracy: 0.8938\n",
      "Epoch: 30/100 Train Loss: 0.2219 Accuracy: 0.9271 Time: 7.16384  | Val Loss: 0.3360 Accuracy: 0.8930\n",
      "Epoch: 31/100 Train Loss: 0.2329 Accuracy: 0.9244 Time: 7.20480  | Val Loss: 0.3300 Accuracy: 0.8989\n",
      "Epoch: 32/100 Train Loss: 0.2263 Accuracy: 0.9283 Time: 7.12146  | Val Loss: 0.3445 Accuracy: 0.8932\n",
      "Epoch: 33/100 Train Loss: 0.2278 Accuracy: 0.9262 Time: 7.14296  | Val Loss: 0.3394 Accuracy: 0.8938\n",
      "Epoch: 34/100 Train Loss: 0.2268 Accuracy: 0.9310 Time: 7.32277  | Val Loss: 0.3383 Accuracy: 0.8935\n",
      "Epoch: 35/100 Train Loss: 0.2024 Accuracy: 0.9360 Time: 7.16445  | Val Loss: 0.3192 Accuracy: 0.9024\n",
      "Epoch: 36/100 Train Loss: 0.2196 Accuracy: 0.9278 Time: 7.05094  | Val Loss: 0.3411 Accuracy: 0.8938\n",
      "Epoch: 37/100 Train Loss: 0.2148 Accuracy: 0.9307 Time: 7.21304  | Val Loss: 0.3223 Accuracy: 0.8983\n",
      "Epoch: 38/100 Train Loss: 0.2031 Accuracy: 0.9342 Time: 7.24524  | Val Loss: 0.3310 Accuracy: 0.8994\n",
      "Epoch: 39/100 Train Loss: 0.1973 Accuracy: 0.9349 Time: 7.30015  | Val Loss: 0.3546 Accuracy: 0.8943\n",
      "Epoch: 40/100 Train Loss: 0.2029 Accuracy: 0.9346 Time: 7.06895  | Val Loss: 0.3407 Accuracy: 0.8938\n",
      "Epoch: 41/100 Train Loss: 0.1953 Accuracy: 0.9370 Time: 7.15550  | Val Loss: 0.3275 Accuracy: 0.8983\n",
      "Epoch: 42/100 Train Loss: 0.1936 Accuracy: 0.9389 Time: 7.05536  | Val Loss: 0.3215 Accuracy: 0.9004\n",
      "Epoch: 43/100 Train Loss: 0.1854 Accuracy: 0.9401 Time: 7.14980  | Val Loss: 0.3274 Accuracy: 0.8989\n",
      "Epoch: 44/100 Train Loss: 0.1867 Accuracy: 0.9402 Time: 7.13137  | Val Loss: 0.3253 Accuracy: 0.9006\n",
      "Epoch: 45/100 Train Loss: 0.1731 Accuracy: 0.9467 Time: 7.08454  | Val Loss: 0.3241 Accuracy: 0.9009\n",
      "Epoch: 46/100 Train Loss: 0.1835 Accuracy: 0.9438 Time: 7.19251  | Val Loss: 0.3245 Accuracy: 0.8996\n",
      "Epoch: 47/100 Train Loss: 0.1856 Accuracy: 0.9439 Time: 7.19701  | Val Loss: 0.3231 Accuracy: 0.9022\n",
      "Epoch: 48/100 Train Loss: 0.1839 Accuracy: 0.9423 Time: 7.37439  | Val Loss: 0.3281 Accuracy: 0.9029\n",
      "Epoch: 49/100 Train Loss: 0.1820 Accuracy: 0.9429 Time: 7.18362  | Val Loss: 0.3188 Accuracy: 0.9055\n",
      "Epoch: 50/100 Train Loss: 0.1822 Accuracy: 0.9432 Time: 7.11288  | Val Loss: 0.3245 Accuracy: 0.9022\n",
      "Epoch: 51/100 Train Loss: 0.1803 Accuracy: 0.9445 Time: 7.29733  | Val Loss: 0.3289 Accuracy: 0.9011\n",
      "Epoch: 52/100 Train Loss: 0.1785 Accuracy: 0.9464 Time: 7.10740  | Val Loss: 0.3258 Accuracy: 0.9014\n",
      "Epoch: 53/100 Train Loss: 0.1740 Accuracy: 0.9427 Time: 7.12927  | Val Loss: 0.3233 Accuracy: 0.9001\n",
      "Epoch: 54/100 Train Loss: 0.1762 Accuracy: 0.9457 Time: 7.10182  | Val Loss: 0.3236 Accuracy: 0.9027\n",
      "Epoch: 55/100 Train Loss: 0.1762 Accuracy: 0.9439 Time: 7.23740  | Val Loss: 0.3241 Accuracy: 0.9014\n",
      "Epoch: 56/100 Train Loss: 0.1783 Accuracy: 0.9441 Time: 7.11916  | Val Loss: 0.3239 Accuracy: 0.9014\n",
      "Epoch: 57/100 Train Loss: 0.1799 Accuracy: 0.9437 Time: 7.08281  | Val Loss: 0.3214 Accuracy: 0.9034\n",
      "Epoch: 58/100 Train Loss: 0.1684 Accuracy: 0.9459 Time: 7.15652  | Val Loss: 0.3221 Accuracy: 0.9024\n",
      "Epoch: 59/100 Train Loss: 0.1870 Accuracy: 0.9414 Time: 7.25389  | Val Loss: 0.3207 Accuracy: 0.9039\n",
      "Epoch: 60/100 Train Loss: 0.1787 Accuracy: 0.9419 Time: 7.17986  | Val Loss: 0.3217 Accuracy: 0.9039\n",
      "Epoch: 61/100 Train Loss: 0.1792 Accuracy: 0.9418 Time: 7.08233  | Val Loss: 0.3243 Accuracy: 0.9037\n",
      "Epoch: 62/100 Train Loss: 0.1805 Accuracy: 0.9452 Time: 7.22222  | Val Loss: 0.3251 Accuracy: 0.9034\n",
      "Epoch: 63/100 Train Loss: 0.1796 Accuracy: 0.9437 Time: 7.31252  | Val Loss: 0.3206 Accuracy: 0.9037\n",
      "Epoch: 64/100 Train Loss: 0.1763 Accuracy: 0.9446 Time: 7.20752  | Val Loss: 0.3224 Accuracy: 0.9037\n",
      "Early stop at epoch 64!\n"
     ]
    }
   ],
   "source": [
    "from typing import Tuple\n",
    "\n",
    "\n",
    "def train_classifier_with_aux(\n",
    "    net: nn.Module,\n",
    "    criteria: nn.CrossEntropyLoss,\n",
    "    optimizer: optim.Optimizer,\n",
    "    train_loader: torch.utils.data.DataLoader,\n",
    "    device: torch.device,\n",
    ") -> Tuple[float, float]:\n",
    "    train_loss = 0.0\n",
    "    correct_items = 0\n",
    "    total_items = 0\n",
    "    net.train()\n",
    "    for Xs, ys in train_loader:\n",
    "        Xs, ys = Xs.to(device), ys.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        logits, aux_logits1, aux_logits2 = net(Xs)\n",
    "        # nn.CrossEntropyLoss是stateless的,可以被重复调用\n",
    "        loss1 = criteria(logits, ys)\n",
    "        loss2 = criteria(aux_logits1, ys)\n",
    "        loss3 = criteria(aux_logits2, ys)\n",
    "        loss = loss1 + 0.3 * loss2 + 0.3 * loss3\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # 为了和Eval loss比较,我们只记录第一个loss\n",
    "        train_loss += loss1.item()\n",
    "        correct_items += torch.sum(torch.argmax(logits, dim=1) == ys).item()\n",
    "        total_items += Xs.shape[0]\n",
    "\n",
    "    avg_train_loss = train_loss / len(train_loader)\n",
    "    accuracy = correct_items / total_items\n",
    "    return avg_train_loss, accuracy\n",
    "\n",
    "\n",
    "net = GoogLeNet(InceptionV1, num_classes=10, dropout=0.5, use_aux=True).to(DEVICE)\n",
    "criteria = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "    optimizer, step_size=20, gamma=0.1, last_epoch=-1\n",
    ")\n",
    "early_stopper = EarlyStoppingInMem(patience=15, verbose=False)\n",
    "max_epochs = 100\n",
    "training_stats_v1_with_aux = naive_train_classification_model(\n",
    "    net,\n",
    "    criteria,\n",
    "    max_epochs,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    DEVICE,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    early_stopper,\n",
    "    verbose=True,\n",
    "    train_classifier=train_classifier_with_aux,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00c6f1ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy V1 with Aux Classifier: 0.9054777070063694\n"
     ]
    }
   ],
   "source": [
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"Accuracy V1 with Aux Classifier: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e3f2ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = training_stats_v1.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(training_stats_v1_with_aux[field], label=\"Without Aux Classifier\", linestyle=\"-\")\n",
    "    plt.plot(training_stats_v1[field], label=\"With Aux Classifier\", linestyle=\"--\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1449e185",
   "metadata": {},
   "source": [
    "### Inception V2\n",
    "\n",
    "V2将V1中的5✖5卷积替换为两个3x3卷积,在保持相同感受野的情况下,这样可以降低模型参数和计算量,此外额外的非线性层可以提出更复杂的特征.\n",
    "\n",
    "![alt text](resources/inceptionV2.png \"Title\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615b134e",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4361f54d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable parameter in GoogLeNet V2:  5683242\n",
      "Epoch: 1/100 Train Loss: 1.8640 Accuracy: 0.3468 Time: 7.54331  | Val Loss: 1.5683 Accuracy: 0.4973\n",
      "Epoch: 2/100 Train Loss: 1.3507 Accuracy: 0.5529 Time: 7.50121  | Val Loss: 1.5499 Accuracy: 0.5192\n",
      "Epoch: 3/100 Train Loss: 1.1445 Accuracy: 0.6297 Time: 7.50311  | Val Loss: 1.1379 Accuracy: 0.6392\n",
      "Epoch: 4/100 Train Loss: 1.0286 Accuracy: 0.6666 Time: 7.58600  | Val Loss: 0.9133 Accuracy: 0.7027\n",
      "Epoch: 5/100 Train Loss: 0.9313 Accuracy: 0.6989 Time: 7.60563  | Val Loss: 1.0483 Accuracy: 0.6836\n",
      "Epoch: 6/100 Train Loss: 0.8474 Accuracy: 0.7300 Time: 7.51462  | Val Loss: 0.8212 Accuracy: 0.7406\n",
      "Epoch: 7/100 Train Loss: 0.8035 Accuracy: 0.7404 Time: 7.52841  | Val Loss: 0.7670 Accuracy: 0.7541\n",
      "Epoch: 8/100 Train Loss: 0.7675 Accuracy: 0.7500 Time: 7.49873  | Val Loss: 0.8147 Accuracy: 0.7437\n",
      "Epoch: 9/100 Train Loss: 0.7187 Accuracy: 0.7636 Time: 7.47993  | Val Loss: 0.8478 Accuracy: 0.7391\n",
      "Epoch: 10/100 Train Loss: 0.6703 Accuracy: 0.7897 Time: 7.45068  | Val Loss: 0.7502 Accuracy: 0.7702\n",
      "Epoch: 11/100 Train Loss: 0.6564 Accuracy: 0.7899 Time: 7.51605  | Val Loss: 0.7669 Accuracy: 0.7717\n",
      "Epoch: 12/100 Train Loss: 0.6155 Accuracy: 0.8006 Time: 7.53824  | Val Loss: 0.6412 Accuracy: 0.7987\n",
      "Epoch: 13/100 Train Loss: 0.5858 Accuracy: 0.8112 Time: 7.55291  | Val Loss: 1.0235 Accuracy: 0.6825\n",
      "Epoch: 14/100 Train Loss: 0.5637 Accuracy: 0.8173 Time: 7.37442  | Val Loss: 0.5870 Accuracy: 0.8132\n",
      "Epoch: 15/100 Train Loss: 0.5430 Accuracy: 0.8207 Time: 7.56802  | Val Loss: 0.6626 Accuracy: 0.7980\n",
      "Epoch: 16/100 Train Loss: 0.5178 Accuracy: 0.8345 Time: 7.62084  | Val Loss: 0.5731 Accuracy: 0.8201\n",
      "Epoch: 17/100 Train Loss: 0.5124 Accuracy: 0.8389 Time: 7.57564  | Val Loss: 0.5633 Accuracy: 0.8275\n",
      "Epoch: 18/100 Train Loss: 0.4862 Accuracy: 0.8419 Time: 7.58258  | Val Loss: 0.5954 Accuracy: 0.8204\n",
      "Epoch: 19/100 Train Loss: 0.4704 Accuracy: 0.8486 Time: 7.59619  | Val Loss: 0.7940 Accuracy: 0.7559\n",
      "Epoch: 20/100 Train Loss: 0.4540 Accuracy: 0.8528 Time: 7.49958  | Val Loss: 0.5798 Accuracy: 0.8262\n",
      "Epoch: 21/100 Train Loss: 0.3599 Accuracy: 0.8844 Time: 7.45448  | Val Loss: 0.3696 Accuracy: 0.8848\n",
      "Epoch: 22/100 Train Loss: 0.3090 Accuracy: 0.9005 Time: 7.34740  | Val Loss: 0.3601 Accuracy: 0.8859\n",
      "Epoch: 23/100 Train Loss: 0.3016 Accuracy: 0.9048 Time: 7.43160  | Val Loss: 0.3614 Accuracy: 0.8876\n",
      "Epoch: 24/100 Train Loss: 0.2895 Accuracy: 0.9077 Time: 7.49556  | Val Loss: 0.3566 Accuracy: 0.8882\n",
      "Epoch: 25/100 Train Loss: 0.2821 Accuracy: 0.9117 Time: 7.34939  | Val Loss: 0.3559 Accuracy: 0.8902\n",
      "Epoch: 26/100 Train Loss: 0.2784 Accuracy: 0.9137 Time: 7.43984  | Val Loss: 0.3502 Accuracy: 0.8904\n",
      "Epoch: 27/100 Train Loss: 0.2679 Accuracy: 0.9149 Time: 7.55227  | Val Loss: 0.3543 Accuracy: 0.8910\n",
      "Epoch: 28/100 Train Loss: 0.2566 Accuracy: 0.9183 Time: 7.40995  | Val Loss: 0.3494 Accuracy: 0.8925\n",
      "Epoch: 29/100 Train Loss: 0.2660 Accuracy: 0.9168 Time: 7.37258  | Val Loss: 0.3533 Accuracy: 0.8889\n",
      "Epoch: 30/100 Train Loss: 0.2527 Accuracy: 0.9172 Time: 7.45652  | Val Loss: 0.3525 Accuracy: 0.8927\n",
      "Epoch: 31/100 Train Loss: 0.2545 Accuracy: 0.9179 Time: 7.48981  | Val Loss: 0.3553 Accuracy: 0.8910\n",
      "Epoch: 32/100 Train Loss: 0.2416 Accuracy: 0.9219 Time: 7.38251  | Val Loss: 0.3509 Accuracy: 0.8930\n",
      "Epoch: 33/100 Train Loss: 0.2407 Accuracy: 0.9201 Time: 7.46924  | Val Loss: 0.3522 Accuracy: 0.8917\n",
      "Epoch: 34/100 Train Loss: 0.2267 Accuracy: 0.9254 Time: 7.27149  | Val Loss: 0.3509 Accuracy: 0.8938\n",
      "Epoch: 35/100 Train Loss: 0.2354 Accuracy: 0.9258 Time: 7.38997  | Val Loss: 0.3612 Accuracy: 0.8897\n",
      "Epoch: 36/100 Train Loss: 0.2369 Accuracy: 0.9257 Time: 7.36530  | Val Loss: 0.3608 Accuracy: 0.8884\n",
      "Epoch: 37/100 Train Loss: 0.2308 Accuracy: 0.9249 Time: 7.44870  | Val Loss: 0.3530 Accuracy: 0.8925\n",
      "Epoch: 38/100 Train Loss: 0.2225 Accuracy: 0.9282 Time: 7.36196  | Val Loss: 0.3477 Accuracy: 0.8917\n",
      "Epoch: 39/100 Train Loss: 0.2153 Accuracy: 0.9319 Time: 7.52662  | Val Loss: 0.3453 Accuracy: 0.8927\n",
      "Epoch: 40/100 Train Loss: 0.2108 Accuracy: 0.9315 Time: 7.51931  | Val Loss: 0.3546 Accuracy: 0.8892\n",
      "Epoch: 41/100 Train Loss: 0.2078 Accuracy: 0.9351 Time: 7.25279  | Val Loss: 0.3469 Accuracy: 0.8907\n",
      "Epoch: 42/100 Train Loss: 0.2127 Accuracy: 0.9327 Time: 7.43785  | Val Loss: 0.3473 Accuracy: 0.8907\n",
      "Epoch: 43/100 Train Loss: 0.2017 Accuracy: 0.9341 Time: 7.37344  | Val Loss: 0.3420 Accuracy: 0.8892\n",
      "Epoch: 44/100 Train Loss: 0.2145 Accuracy: 0.9323 Time: 7.60952  | Val Loss: 0.3445 Accuracy: 0.8904\n",
      "Epoch: 45/100 Train Loss: 0.1955 Accuracy: 0.9380 Time: 7.51550  | Val Loss: 0.3457 Accuracy: 0.8920\n",
      "Epoch: 46/100 Train Loss: 0.2026 Accuracy: 0.9347 Time: 7.29270  | Val Loss: 0.3479 Accuracy: 0.8915\n",
      "Epoch: 47/100 Train Loss: 0.1976 Accuracy: 0.9359 Time: 7.51696  | Val Loss: 0.3427 Accuracy: 0.8910\n",
      "Epoch: 48/100 Train Loss: 0.1954 Accuracy: 0.9393 Time: 7.48857  | Val Loss: 0.3406 Accuracy: 0.8932\n",
      "Epoch: 49/100 Train Loss: 0.1948 Accuracy: 0.9385 Time: 7.32696  | Val Loss: 0.3402 Accuracy: 0.8922\n",
      "Epoch: 50/100 Train Loss: 0.1871 Accuracy: 0.9417 Time: 7.51753  | Val Loss: 0.3437 Accuracy: 0.8932\n",
      "Epoch: 51/100 Train Loss: 0.1947 Accuracy: 0.9400 Time: 7.49260  | Val Loss: 0.3475 Accuracy: 0.8935\n",
      "Epoch: 52/100 Train Loss: 0.1830 Accuracy: 0.9429 Time: 7.56632  | Val Loss: 0.3450 Accuracy: 0.8930\n",
      "Epoch: 53/100 Train Loss: 0.1893 Accuracy: 0.9419 Time: 7.44383  | Val Loss: 0.3403 Accuracy: 0.8943\n",
      "Epoch: 54/100 Train Loss: 0.1951 Accuracy: 0.9393 Time: 7.46252  | Val Loss: 0.3372 Accuracy: 0.8950\n",
      "Epoch: 55/100 Train Loss: 0.1975 Accuracy: 0.9378 Time: 7.44793  | Val Loss: 0.3436 Accuracy: 0.8925\n",
      "Epoch: 56/100 Train Loss: 0.2009 Accuracy: 0.9347 Time: 7.30517  | Val Loss: 0.3399 Accuracy: 0.8940\n",
      "Epoch: 57/100 Train Loss: 0.1958 Accuracy: 0.9398 Time: 7.67839  | Val Loss: 0.3415 Accuracy: 0.8971\n",
      "Epoch: 58/100 Train Loss: 0.1827 Accuracy: 0.9419 Time: 7.49113  | Val Loss: 0.3394 Accuracy: 0.8945\n",
      "Epoch: 59/100 Train Loss: 0.1867 Accuracy: 0.9389 Time: 7.38892  | Val Loss: 0.3454 Accuracy: 0.8922\n",
      "Epoch: 60/100 Train Loss: 0.1852 Accuracy: 0.9397 Time: 7.49041  | Val Loss: 0.3404 Accuracy: 0.8932\n",
      "Epoch: 61/100 Train Loss: 0.1816 Accuracy: 0.9408 Time: 7.55792  | Val Loss: 0.3412 Accuracy: 0.8935\n",
      "Epoch: 62/100 Train Loss: 0.1923 Accuracy: 0.9414 Time: 7.36581  | Val Loss: 0.3383 Accuracy: 0.8932\n",
      "Epoch: 63/100 Train Loss: 0.1895 Accuracy: 0.9376 Time: 7.48393  | Val Loss: 0.3395 Accuracy: 0.8948\n",
      "Epoch: 64/100 Train Loss: 0.1781 Accuracy: 0.9428 Time: 7.44228  | Val Loss: 0.3374 Accuracy: 0.8945\n",
      "Epoch: 65/100 Train Loss: 0.1921 Accuracy: 0.9383 Time: 7.36789  | Val Loss: 0.3416 Accuracy: 0.8943\n",
      "Epoch: 66/100 Train Loss: 0.1864 Accuracy: 0.9414 Time: 7.41383  | Val Loss: 0.3419 Accuracy: 0.8958\n",
      "Epoch: 67/100 Train Loss: 0.1915 Accuracy: 0.9411 Time: 7.56302  | Val Loss: 0.3406 Accuracy: 0.8948\n",
      "Epoch: 68/100 Train Loss: 0.1844 Accuracy: 0.9413 Time: 7.50663  | Val Loss: 0.3392 Accuracy: 0.8961\n",
      "Epoch: 69/100 Train Loss: 0.1913 Accuracy: 0.9402 Time: 7.49181  | Val Loss: 0.3440 Accuracy: 0.8938\n",
      "Early stop at epoch 69!\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.googlenet import InceptionV2\n",
    "\n",
    "net = GoogLeNet(InceptionV2, num_classes=10, dropout=0.5, use_aux=False).to(DEVICE)\n",
    "print(\"Trainable parameter in GoogLeNet V2: \", count_trainable_parameter(net))\n",
    "criteria = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "    optimizer, step_size=20, gamma=0.1, last_epoch=-1\n",
    ")\n",
    "early_stopper = EarlyStoppingInMem(patience=15, verbose=False)\n",
    "max_epochs = 100\n",
    "training_stats_v2 = naive_train_classification_model(\n",
    "    net,\n",
    "    criteria,\n",
    "    max_epochs,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    DEVICE,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    early_stopper,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10ce4154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy V2: 0.8950318471337579\n"
     ]
    }
   ],
   "source": [
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"Accuracy V2: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fd2ad7",
   "metadata": {},
   "source": [
    "### Inception V3\n",
    "\n",
    "V3将V2中的nxn卷积分解为为两个(nx1,1xn)卷积,这个思路和图像处理中加速Gaussian kernel计算类似,Gaussian Kernel可分离为两个一维卷积.\n",
    "\n",
    "![alt text](resources/inceptionV3.png \"Title\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e01d058",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable parameter in GoogLeNet V3:  4067754\n",
      "Epoch: 1/100 Train Loss: 2.0615 Accuracy: 0.2572 Time: 7.55883  | Val Loss: 1.8972 Accuracy: 0.3363\n",
      "Epoch: 2/100 Train Loss: 1.7014 Accuracy: 0.4131 Time: 7.41424  | Val Loss: 1.4619 Accuracy: 0.5022\n",
      "Epoch: 3/100 Train Loss: 1.4426 Accuracy: 0.5176 Time: 7.39287  | Val Loss: 1.4019 Accuracy: 0.5498\n",
      "Epoch: 4/100 Train Loss: 1.2868 Accuracy: 0.5745 Time: 7.42444  | Val Loss: 1.2250 Accuracy: 0.5992\n",
      "Epoch: 5/100 Train Loss: 1.1558 Accuracy: 0.6269 Time: 7.50499  | Val Loss: 1.3352 Accuracy: 0.5896\n",
      "Epoch: 6/100 Train Loss: 1.0722 Accuracy: 0.6479 Time: 7.54082  | Val Loss: 1.4504 Accuracy: 0.5766\n",
      "Epoch: 7/100 Train Loss: 0.9986 Accuracy: 0.6786 Time: 7.37050  | Val Loss: 1.0726 Accuracy: 0.6690\n",
      "Epoch: 8/100 Train Loss: 0.9250 Accuracy: 0.7038 Time: 7.50643  | Val Loss: 1.4303 Accuracy: 0.5783\n",
      "Epoch: 9/100 Train Loss: 0.8779 Accuracy: 0.7180 Time: 7.40980  | Val Loss: 1.0169 Accuracy: 0.6777\n",
      "Epoch: 10/100 Train Loss: 0.8181 Accuracy: 0.7367 Time: 7.45749  | Val Loss: 0.9049 Accuracy: 0.7118\n",
      "Epoch: 11/100 Train Loss: 0.7831 Accuracy: 0.7544 Time: 7.42283  | Val Loss: 1.0966 Accuracy: 0.6652\n",
      "Epoch: 12/100 Train Loss: 0.7650 Accuracy: 0.7550 Time: 7.38438  | Val Loss: 0.7922 Accuracy: 0.7450\n",
      "Epoch: 13/100 Train Loss: 0.7202 Accuracy: 0.7689 Time: 7.32069  | Val Loss: 0.6650 Accuracy: 0.7906\n",
      "Epoch: 14/100 Train Loss: 0.6898 Accuracy: 0.7804 Time: 7.39304  | Val Loss: 0.8963 Accuracy: 0.7287\n",
      "Epoch: 15/100 Train Loss: 0.6592 Accuracy: 0.7921 Time: 7.33363  | Val Loss: 0.7205 Accuracy: 0.7819\n",
      "Epoch: 16/100 Train Loss: 0.6241 Accuracy: 0.7970 Time: 7.48024  | Val Loss: 0.7537 Accuracy: 0.7575\n",
      "Epoch: 17/100 Train Loss: 0.6276 Accuracy: 0.7989 Time: 7.41043  | Val Loss: 0.7399 Accuracy: 0.7710\n",
      "Epoch: 18/100 Train Loss: 0.5864 Accuracy: 0.8131 Time: 7.52026  | Val Loss: 0.6326 Accuracy: 0.7985\n",
      "Epoch: 19/100 Train Loss: 0.5597 Accuracy: 0.8184 Time: 7.42144  | Val Loss: 0.7113 Accuracy: 0.7753\n",
      "Epoch: 20/100 Train Loss: 0.5468 Accuracy: 0.8221 Time: 7.38939  | Val Loss: 0.7439 Accuracy: 0.7743\n",
      "Epoch: 21/100 Train Loss: 0.4518 Accuracy: 0.8561 Time: 7.54388  | Val Loss: 0.4445 Accuracy: 0.8604\n",
      "Epoch: 22/100 Train Loss: 0.4021 Accuracy: 0.8718 Time: 7.53422  | Val Loss: 0.4383 Accuracy: 0.8637\n",
      "Epoch: 23/100 Train Loss: 0.3868 Accuracy: 0.8753 Time: 7.32541  | Val Loss: 0.4360 Accuracy: 0.8650\n",
      "Epoch: 24/100 Train Loss: 0.3657 Accuracy: 0.8835 Time: 7.43232  | Val Loss: 0.4246 Accuracy: 0.8665\n",
      "Epoch: 25/100 Train Loss: 0.3601 Accuracy: 0.8815 Time: 7.33648  | Val Loss: 0.4168 Accuracy: 0.8690\n",
      "Epoch: 26/100 Train Loss: 0.3614 Accuracy: 0.8815 Time: 7.38862  | Val Loss: 0.4251 Accuracy: 0.8652\n",
      "Epoch: 27/100 Train Loss: 0.3547 Accuracy: 0.8832 Time: 7.38946  | Val Loss: 0.4232 Accuracy: 0.8647\n",
      "Epoch: 28/100 Train Loss: 0.3421 Accuracy: 0.8891 Time: 7.52376  | Val Loss: 0.4180 Accuracy: 0.8729\n",
      "Epoch: 29/100 Train Loss: 0.3469 Accuracy: 0.8866 Time: 7.41426  | Val Loss: 0.4162 Accuracy: 0.8685\n",
      "Epoch: 30/100 Train Loss: 0.3409 Accuracy: 0.8889 Time: 7.37506  | Val Loss: 0.4058 Accuracy: 0.8724\n",
      "Epoch: 31/100 Train Loss: 0.3418 Accuracy: 0.8922 Time: 7.40440  | Val Loss: 0.4245 Accuracy: 0.8668\n",
      "Epoch: 32/100 Train Loss: 0.3360 Accuracy: 0.8883 Time: 7.39021  | Val Loss: 0.4198 Accuracy: 0.8708\n",
      "Epoch: 33/100 Train Loss: 0.3325 Accuracy: 0.8928 Time: 7.50609  | Val Loss: 0.4170 Accuracy: 0.8678\n",
      "Epoch: 34/100 Train Loss: 0.3214 Accuracy: 0.8970 Time: 7.46657  | Val Loss: 0.4096 Accuracy: 0.8726\n",
      "Epoch: 35/100 Train Loss: 0.3104 Accuracy: 0.9013 Time: 7.45253  | Val Loss: 0.4108 Accuracy: 0.8726\n",
      "Epoch: 36/100 Train Loss: 0.3115 Accuracy: 0.9031 Time: 7.55009  | Val Loss: 0.4122 Accuracy: 0.8724\n",
      "Epoch: 37/100 Train Loss: 0.3108 Accuracy: 0.9013 Time: 7.55614  | Val Loss: 0.4187 Accuracy: 0.8706\n",
      "Epoch: 38/100 Train Loss: 0.2953 Accuracy: 0.9040 Time: 7.55372  | Val Loss: 0.4163 Accuracy: 0.8701\n",
      "Epoch: 39/100 Train Loss: 0.2997 Accuracy: 0.9027 Time: 7.43034  | Val Loss: 0.4239 Accuracy: 0.8703\n",
      "Epoch: 40/100 Train Loss: 0.2897 Accuracy: 0.9075 Time: 7.49458  | Val Loss: 0.4281 Accuracy: 0.8627\n",
      "Epoch: 41/100 Train Loss: 0.2884 Accuracy: 0.9071 Time: 7.44902  | Val Loss: 0.4074 Accuracy: 0.8754\n",
      "Epoch: 42/100 Train Loss: 0.2821 Accuracy: 0.9071 Time: 7.46554  | Val Loss: 0.4102 Accuracy: 0.8711\n",
      "Epoch: 43/100 Train Loss: 0.2758 Accuracy: 0.9140 Time: 7.50493  | Val Loss: 0.4069 Accuracy: 0.8696\n",
      "Epoch: 44/100 Train Loss: 0.2686 Accuracy: 0.9129 Time: 7.47340  | Val Loss: 0.4026 Accuracy: 0.8767\n",
      "Epoch: 45/100 Train Loss: 0.2725 Accuracy: 0.9145 Time: 7.32901  | Val Loss: 0.4055 Accuracy: 0.8762\n",
      "Epoch: 46/100 Train Loss: 0.2729 Accuracy: 0.9131 Time: 7.55291  | Val Loss: 0.4067 Accuracy: 0.8749\n",
      "Epoch: 47/100 Train Loss: 0.2766 Accuracy: 0.9111 Time: 7.39593  | Val Loss: 0.4057 Accuracy: 0.8759\n",
      "Epoch: 48/100 Train Loss: 0.2646 Accuracy: 0.9154 Time: 7.37463  | Val Loss: 0.4046 Accuracy: 0.8741\n",
      "Epoch: 49/100 Train Loss: 0.2787 Accuracy: 0.9113 Time: 7.48000  | Val Loss: 0.4007 Accuracy: 0.8777\n",
      "Epoch: 50/100 Train Loss: 0.2636 Accuracy: 0.9193 Time: 7.44487  | Val Loss: 0.4000 Accuracy: 0.8772\n",
      "Epoch: 51/100 Train Loss: 0.2712 Accuracy: 0.9145 Time: 7.51414  | Val Loss: 0.4058 Accuracy: 0.8759\n",
      "Epoch: 52/100 Train Loss: 0.2673 Accuracy: 0.9136 Time: 7.37003  | Val Loss: 0.4043 Accuracy: 0.8746\n",
      "Epoch: 53/100 Train Loss: 0.2659 Accuracy: 0.9151 Time: 7.46480  | Val Loss: 0.4052 Accuracy: 0.8775\n",
      "Epoch: 54/100 Train Loss: 0.2651 Accuracy: 0.9175 Time: 7.44441  | Val Loss: 0.4059 Accuracy: 0.8762\n",
      "Epoch: 55/100 Train Loss: 0.2743 Accuracy: 0.9125 Time: 7.40516  | Val Loss: 0.3992 Accuracy: 0.8772\n",
      "Epoch: 56/100 Train Loss: 0.2632 Accuracy: 0.9160 Time: 7.31103  | Val Loss: 0.4034 Accuracy: 0.8757\n",
      "Epoch: 57/100 Train Loss: 0.2615 Accuracy: 0.9168 Time: 7.48138  | Val Loss: 0.4008 Accuracy: 0.8782\n",
      "Epoch: 58/100 Train Loss: 0.2653 Accuracy: 0.9121 Time: 7.54746  | Val Loss: 0.4016 Accuracy: 0.8752\n",
      "Epoch: 59/100 Train Loss: 0.2685 Accuracy: 0.9135 Time: 7.41472  | Val Loss: 0.4012 Accuracy: 0.8782\n",
      "Epoch: 60/100 Train Loss: 0.2727 Accuracy: 0.9111 Time: 7.38035  | Val Loss: 0.4019 Accuracy: 0.8785\n",
      "Epoch: 61/100 Train Loss: 0.2606 Accuracy: 0.9170 Time: 7.44451  | Val Loss: 0.4018 Accuracy: 0.8787\n",
      "Epoch: 62/100 Train Loss: 0.2637 Accuracy: 0.9179 Time: 7.48409  | Val Loss: 0.4035 Accuracy: 0.8764\n",
      "Epoch: 63/100 Train Loss: 0.2621 Accuracy: 0.9151 Time: 7.51281  | Val Loss: 0.4025 Accuracy: 0.8787\n",
      "Epoch: 64/100 Train Loss: 0.2609 Accuracy: 0.9156 Time: 7.46945  | Val Loss: 0.4023 Accuracy: 0.8777\n",
      "Epoch: 65/100 Train Loss: 0.2617 Accuracy: 0.9161 Time: 7.53143  | Val Loss: 0.3990 Accuracy: 0.8790\n",
      "Epoch: 66/100 Train Loss: 0.2562 Accuracy: 0.9202 Time: 7.46484  | Val Loss: 0.4049 Accuracy: 0.8762\n",
      "Epoch: 67/100 Train Loss: 0.2601 Accuracy: 0.9150 Time: 7.38837  | Val Loss: 0.4001 Accuracy: 0.8790\n",
      "Epoch: 68/100 Train Loss: 0.2593 Accuracy: 0.9174 Time: 7.35827  | Val Loss: 0.3992 Accuracy: 0.8795\n",
      "Epoch: 69/100 Train Loss: 0.2613 Accuracy: 0.9155 Time: 7.43893  | Val Loss: 0.4013 Accuracy: 0.8757\n",
      "Epoch: 70/100 Train Loss: 0.2661 Accuracy: 0.9138 Time: 7.44024  | Val Loss: 0.4019 Accuracy: 0.8777\n",
      "Epoch: 71/100 Train Loss: 0.2637 Accuracy: 0.9144 Time: 7.43217  | Val Loss: 0.4006 Accuracy: 0.8780\n",
      "Epoch: 72/100 Train Loss: 0.2494 Accuracy: 0.9213 Time: 7.46407  | Val Loss: 0.4030 Accuracy: 0.8782\n",
      "Epoch: 73/100 Train Loss: 0.2729 Accuracy: 0.9142 Time: 7.51974  | Val Loss: 0.4030 Accuracy: 0.8769\n",
      "Epoch: 74/100 Train Loss: 0.2659 Accuracy: 0.9150 Time: 7.53248  | Val Loss: 0.4041 Accuracy: 0.8757\n",
      "Epoch: 75/100 Train Loss: 0.2501 Accuracy: 0.9187 Time: 7.49379  | Val Loss: 0.4052 Accuracy: 0.8762\n",
      "Epoch: 76/100 Train Loss: 0.2621 Accuracy: 0.9173 Time: 7.47520  | Val Loss: 0.3979 Accuracy: 0.8797\n",
      "Epoch: 77/100 Train Loss: 0.2588 Accuracy: 0.9177 Time: 7.37744  | Val Loss: 0.4013 Accuracy: 0.8777\n",
      "Epoch: 78/100 Train Loss: 0.2634 Accuracy: 0.9154 Time: 7.51648  | Val Loss: 0.4030 Accuracy: 0.8752\n",
      "Epoch: 79/100 Train Loss: 0.2537 Accuracy: 0.9219 Time: 7.47432  | Val Loss: 0.4017 Accuracy: 0.8767\n",
      "Epoch: 80/100 Train Loss: 0.2666 Accuracy: 0.9141 Time: 7.40458  | Val Loss: 0.3991 Accuracy: 0.8790\n",
      "Epoch: 81/100 Train Loss: 0.2705 Accuracy: 0.9111 Time: 7.58849  | Val Loss: 0.4071 Accuracy: 0.8754\n",
      "Epoch: 82/100 Train Loss: 0.2561 Accuracy: 0.9190 Time: 7.43617  | Val Loss: 0.4049 Accuracy: 0.8775\n",
      "Epoch: 83/100 Train Loss: 0.2519 Accuracy: 0.9203 Time: 7.33439  | Val Loss: 0.4037 Accuracy: 0.8759\n",
      "Epoch: 84/100 Train Loss: 0.2619 Accuracy: 0.9193 Time: 7.37840  | Val Loss: 0.4013 Accuracy: 0.8782\n",
      "Epoch: 85/100 Train Loss: 0.2636 Accuracy: 0.9148 Time: 7.43285  | Val Loss: 0.4023 Accuracy: 0.8775\n",
      "Epoch: 86/100 Train Loss: 0.2596 Accuracy: 0.9166 Time: 7.42641  | Val Loss: 0.4009 Accuracy: 0.8754\n",
      "Epoch: 87/100 Train Loss: 0.2789 Accuracy: 0.9107 Time: 7.30931  | Val Loss: 0.3997 Accuracy: 0.8792\n",
      "Epoch: 88/100 Train Loss: 0.2536 Accuracy: 0.9164 Time: 7.39932  | Val Loss: 0.4022 Accuracy: 0.8782\n",
      "Epoch: 89/100 Train Loss: 0.2644 Accuracy: 0.9153 Time: 7.52581  | Val Loss: 0.4013 Accuracy: 0.8772\n",
      "Epoch: 90/100 Train Loss: 0.2565 Accuracy: 0.9178 Time: 7.41272  | Val Loss: 0.4061 Accuracy: 0.8775\n",
      "Epoch: 91/100 Train Loss: 0.2583 Accuracy: 0.9185 Time: 7.37805  | Val Loss: 0.3993 Accuracy: 0.8792\n",
      "Early stop at epoch 91!\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.googlenet import InceptionV3\n",
    "\n",
    "net = GoogLeNet(InceptionV3, num_classes=10, dropout=0.5, use_aux=False).to(DEVICE)\n",
    "print(\"Trainable parameter in GoogLeNet V3: \", count_trainable_parameter(net))\n",
    "criteria = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "    optimizer, step_size=20, gamma=0.1, last_epoch=-1\n",
    ")\n",
    "early_stopper = EarlyStoppingInMem(patience=15, verbose=False)\n",
    "max_epochs = 100\n",
    "training_stats_v3 = naive_train_classification_model(\n",
    "    net,\n",
    "    criteria,\n",
    "    max_epochs,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    DEVICE,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    early_stopper,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15dde82b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy V3: 0.8797452229299363\n"
     ]
    }
   ],
   "source": [
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"Accuracy V3: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1658b4ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = training_stats_v1.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(training_stats_v1[field], label=\"V1\", linestyle=\"-\")\n",
    "    plt.plot(training_stats_v2[field], label=\"V2\", linestyle=\"--\")\n",
    "    plt.plot(training_stats_v3[field], label=\"V3\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceec35d7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
